DeepVol: Volatility Forecasting from High-Frequency Data with Dilated Causal Convolutions

TL;DR

DeepVol leverages dilated causal convolutions on high-frequency intraday data to improve day-ahead volatility forecasts, outperforming traditional models by capturing more relevant information and avoiding manual feature engineering.

Contribution

This paper introduces DeepVol, a novel deep learning model using dilated causal convolutions to effectively utilize high-frequency data for volatility prediction.

Findings

DeepVol outperforms traditional models in forecasting accuracy.

Dilated convolutions effectively extract relevant features from intraday data.

High-frequency data improves risk measure precision.

Abstract

Volatility forecasts play a central role among equity risk measures. Besides traditional statistical models, modern forecasting techniques based on machine learning can be employed when treating volatility as a univariate, daily time-series. Moreover, econometric studies have shown that increasing the number of daily observations with high-frequency intraday data helps to improve volatility predictions. In this work, we propose DeepVol, a model based on Dilated Causal Convolutions that uses high-frequency data to forecast day-ahead volatility. Our empirical findings demonstrate that dilated convolutional filters are highly effective at extracting relevant information from intraday financial time-series, proving that this architecture can effectively leverage predictive information present in high-frequency data that would otherwise be lost if realised measures were precomputed. Simultaneously, dilated convolutional filters trained with intraday high-frequency data help us avoid the limitations of models that use daily data, such as model misspecification or manually designed handcrafted features, whose devise involves optimising the trade-off between accuracy and computational efficiency and makes models prone to lack of adaptation into changing circumstances. In our analysis, we use two years of intraday data from NASDAQ-100 to evaluate the performance of DeepVol. Our empirical results suggest that the proposed deep learning-based approach effectively learns global features from high-frequency data, resulting in more accurate predictions compared to traditional methodologies and producing more accurate risk measures.